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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Purposive Learning01:22

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Related Experiment Video

Updated: Aug 29, 2025

A Two-interval Forced-choice Task for Multisensory Comparisons
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A multi-intent based multi-policy relay contrastive learning for sequential recommendation.

Weiqiang Di1

  • 1School of Computer and Information Technology, Beijing Jiaotong University, Beijing, Beijing, China.

Peerj. Computer Science
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for sequential recommendations that addresses data sparsity by learning multiple user intents. A novel multi-policy relay training strategy improves contrastive learning performance for better recommendations.

Keywords:
Contrastive learningSequential recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Sequential recommendation systems aim to capture dynamic user preferences but struggle with sparse data.
  • Contrastive learning (CL) shows promise in addressing data sparsity by improving item representations.
  • Learning influential latent intents is crucial for understanding sequence evolution in user behavior.

Purpose of the Study:

  • To develop a novel approach for sequential recommendations that effectively handles data sparsity.
  • To enhance the learning of user representations by focusing on multiple latent intents.
  • To improve the cooperation and effectiveness of multiple contrastive learning tasks.

Main Methods:

  • A novel multi-intent self-attention module is proposed to decompose user behavior sequences into distinct latent intents.
  • A multi-policy relay training strategy is introduced to manage and optimize multiple contrastive learning tasks in stages.
  • The model is evaluated on four public recommendation datasets.

Main Results:

  • The proposed multi-intent self-attention module effectively captures diverse user preferences from sparse data.
  • The multi-policy relay training strategy enhances the performance of contrastive learning by enabling better cooperation between different data augmentations.
  • Experimental results demonstrate the superiority of the proposed model over existing methods.

Conclusions:

  • The novel approach significantly improves sequential recommendation performance, especially in sparse data scenarios.
  • The combination of multi-intent self-attention and multi-policy relay training offers a robust solution for data sparsity challenges.
  • This work provides a valuable contribution to the field of sequential recommendations and contrastive learning.